The Optimal Supply Decision Based on Dynamic Multiobjective Optimization and Prediction

Algorithm 1

Algorithm for dynamic multiobjective optimization.

1. INPUT: Population P, Maximum Iteration, Time Step(t=0), Evolution Generation(i=1)

2. OUTPUT: Approximated POF

3. BEGIN

4.    Randomly initialize a population P

5.    Iterate (until Maximum Iteration)

6.    If change has appeared

7.       t=t+1

8.       Collect old and new environment information

9.       If evolution generation meet the adjustment conditions

10.         Calculate the new allocation ratio score

11.         Aggregate the scores and calculate the rank

12.         Exact search results for different empowerment goals

13.         Comparing weight combinations and optimal solutions

14.    Perform the domination process among population, i=i+1

15.    Select the highest domination and crowding distance as output POF

16.END